Using a hearable to generate a user health indicator based on user temperature

ABSTRACT

A hearable comprises a wearable structure including a speaker, a sensor, and a temperature compensating circuit which measures temperature in an environment of the sensor. A portion of the wearable structure, which includes the sensor and temperature compensating circuit, is disposed within a user’s ear when in use. A sensor processing unit which is communicatively coupled with the temperature compensating circuit: acquires temperature data from the temperature compensating circuit while the portion of the wearable structure is disposed within the ear of the user; builds a baseline model of normal temperature for the user; and compares a temperature measurement acquired from the temperature compensating circuit to the baseline model. In response to the comparison showing a deviation beyond a preset threshold from the baseline model, the sensor processing unit generates a health indicator for the user which is used to monitor an aspect of health of the user.

CROSS-REFERENCE TO RELATED APPLICATION - CONTINUATION-IN-PART

This application is a continuation/continuation-in-part application ofand claims priority to and benefit of co-pending U.S. Pat. ApplicationNo. 16/265,918 filed on Feb. 1, 2019 entitled “USING A HEARABLE TOGENERATE A USER HEALTH INDICATOR” by Jibran Ahmed et al., havingAttorney Docket No. IVS-797, and assigned to the assignee of the presentapplication, the disclosure of which is hereby incorporated herein byreference in its entirety.

Application 16/265,918 claims priority to and benefit of then co-pendingU.S. Provisional Pat. Application No. 62/624,834 filed on Feb. 1, 2018entitled “HEARING INSTRUMENTS COMPRISING MOTION SENSORS” by KarthikKatingari et al., having Attorney Docket No. IVS-797-PR, and assigned tothe assignee of the present application, the disclosure of which ishereby incorporated herein by reference in its entirety.

BACKGROUND

A hearing instrument (HI), also referred to as a hearing aid or“hearable,” is a device designed to reproduce sound from a recordedsource and/or improve hearing by making sound audible to a person withhearing loss. A hearable may or may not be a medical device. In someembodiments, hearable which is not a medical device may be an enhancedheadphone which is used for listening to phone calls, music, and thelike provided to the hearable via communication with an electronicdevice. A hearable in general comprises a microphone and speakercombination, along with a processor to process the signal captured bythe microphone and to control the output of the speaker. A hearable mayinclude additional features such as, for example touch sensors, whichpermit additional functionality. A hearable may also be coupled(typically wirelessly) with another hearable and/or an electronic devicesuch as a computer, smartphone, smartwatch, or a tablet computer.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and form a part ofthe Description of Embodiments, illustrate various embodiments of thesubject matter and, together with the Description of Embodiments, serveto explain principles of the subject matter discussed below. Unlessspecifically noted, the drawings referred to in this Brief Descriptionof Drawings should be understood as not being drawn to scale. Herein,like items are labeled with like item numbers.

FIG. 1 shows a diagram of a human user wearing a pair of hearables inthe ears of the user, in accordance with various aspects of the presentdisclosure.

FIG. 2 illustrates the external configuration of an example hearable, inaccordance with various aspects of the present disclosure.

FIG. 3 shows a block diagram of components of an example hearable, inaccordance with various aspects of the present disclosure.

FIG. 4 illustrates a flow diagram of the operation of a hearable toclassify an activity of the head of a user, in accordance with variousaspects of the present disclosure

FIG. 5 illustrates a block diagram of a sensor with a temperaturecompensating circuit, in accordance with various aspects of the presentdisclosure.

FIG. 6 illustrates a flow diagram of an example method of hearable use,in accordance with various aspects of the present disclosure.

FIGS. 7A-7C illustrate a flow diagram of an example method of hearableuse to generate a user health indicator based on user temperature, inaccordance with various aspects of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Reference will now be made in detail to various embodiments of thesubject matter, examples of which are illustrated in the accompanyingdrawings. While various embodiments are discussed herein, it will beunderstood that they are not intended to limit to these embodiments. Onthe contrary, the presented embodiments are intended to coveralternatives, modifications and equivalents, which may be includedwithin the spirit and scope the various embodiments as defined by theappended claims. Furthermore, in this Description of Embodiments,numerous specific details are set forth in order to provide a thoroughunderstanding of embodiments of the present subject matter. However,embodiments may be practiced without these specific details. In otherinstances, well known methods, procedures, components, and circuits havenot been described in detail as not to unnecessarily obscure aspects ofthe described embodiments.

Overview of Discussion

A variety of uses of hearables are described which add or extend thefunctionality of hearables in ways which quantify the health of a userof the hearable(s). In addition to the typical speaker, microphone, andprocessor, the described hearables include additional sensors, such asmotion sensors (e.g., accelerometers, gyroscopes, magnetometers,inertial sensors, and/or pressure sensors) and, in some embodiments, oneor more additional microphones. The additional sensors may improve theperformance of the hearable and/or provide additional functionalities.This disclosure discusses functionalities that can be added to thehearable(s) by using the hearable(s) for acquiring head motion data andanalyzing it alone or in combination with audio data acquired with thehearable(s). For example, analyzing data acquired via the hearable(s)facilitates generating one or more health indicators for the user whichcan be used to rate or compare an aspect of the user’s health to astandard or benchmark or to one or more previously generated healthindicators for the user.

Discussion begins with a description of notation and nomenclature.Discussion continues with description of a diagram of a human userwearing a pair of hearables in the ears of the user. An example hearableits components are described. Finally, operation of a hearable (or pairof hearables), and components thereof, is discussed in conjunction withdescription of an example method of hearable use and in conjunction withan example method of using a hearable to generate a user healthindicator based on user temperature.

Notation and Nomenclature

Some portions of the detailed descriptions which follow are presented interms of procedures, logic blocks, processes, modules and other symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the means used by thoseskilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. In the presentapplication, a procedure, logic block, process, module, or the like, isconceived to be one or more self-consistent procedures or instructionsleading to a desired result. The procedures are those requiring physicalmanipulations of physical quantities. Usually, although not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated in an electronic device/component.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the description ofembodiments, discussions utilizing terms such as “acquiring,”“separating,” “synchronizing,” “classifying,” “generating,”“determining,” “adjusting,” “filtering,” “using,” “comparing,”“building,” “computing,” “reporting,” “monitoring,” “measuring,”“sensing,” or the like, refer to the actions and processes of anelectronic device or component such as: a hearable, a pair of hearables,a processor of a hearable, a sensor processing unit, a sensor processor,a memory, or the like, or a combination thereof. The electronicdevice/component manipulates and transforms data represented as physical(electronic and/or magnetic) quantities within the registers andmemories into other data similarly represented as physical quantitieswithin memories or registers or other such information storage,transmission, processing, or display components.

Embodiments described herein may be discussed in the general context ofprocessor-executable instructions residing on some form ofnon-transitory processor-readable medium, such as program modules orlogic, executed by one or more computers, processors, or other devices.Generally, program modules include routines, programs, objects,components, data structures, etc., that perform particular tasks orimplement particular abstract data types. The functionality of theprogram modules may be combined or distributed as desired in variousembodiments.

In the figures, a single block may be described as performing a functionor functions; however, in actual practice, the function or functionsperformed by that block may be performed in a single component or acrossmultiple components, and/or may be performed using hardware, usingsoftware, or using a combination of hardware and software. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure. Also, the example electronic device(s)described herein may include components other than those shown,including well-known components.

The techniques described herein may be implemented in hardware, or acombination of hardware with firmware and/or software, unlessspecifically described as being implemented in a specific manner. Anyfeatures described as modules or components may also be implementedtogether in an integrated logic device or separately as discrete butinteroperable logic devices. If implemented in software, the techniquesmay be realized at least in part by a non-transitory processor-readablestorage medium comprising instructions that, when executed, cause aprocessor and/or other components to perform one or more of the methodsdescribed herein. The non-transitory processor-readable data storagemedium may form part of a computer program product, which may includepackaging materials.

The non-transitory processor-readable storage medium may comprise randomaccess memory (RAM) such as synchronous dynamic random access memory(SDRAM), read only memory (ROM), non-volatile random access memory(NVRAM), electrically erasable programmable read-only memory (EEPROM),flash memory, other known storage media, and the like. The techniquesadditionally, or alternatively, may be realized at least in part by aprocessor-readable communication medium that carries or communicatescode in the form of instructions or data structures and that can beaccessed, read, and/or executed by a computer or other processor.

The various illustrative logical blocks, modules, circuits andinstructions described in connection with the embodiments disclosedherein may be executed by one or more processors, such as hostprocessor(s) or core(s) thereof, digital signal processors (DSPs),general purpose microprocessors, application specific integratedcircuits (ASICs), application specific instruction set processors(ASIPs), field programmable gate arrays (FPGAs), or other equivalentintegrated or discrete logic circuitry. The term “processor,” as usedherein may refer to any of the foregoing structures or any otherstructure suitable for implementation of the techniques describedherein. In addition, in some aspects, the functionality described hereinmay be provided within dedicated software modules or hardware modulesconfigured as described herein. Also, the techniques could be fullyimplemented in one or more circuits or logic elements. A general purposeprocessor may be a microprocessor, but in the alternative, the processormay be any conventional processor, controller, microcontroller, or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a plurality of microprocessors, one or moremicroprocessors in conjunction with an ASIC, or any other suchconfiguration.

In various example embodiments discussed herein, a chip is defined toinclude at least one substrate typically formed from a semiconductormaterial. A single chip may for example be formed from multiplesubstrates, where the substrates are mechanically bonded to preserve thefunctionality. Multiple chip (or multi-chip) includes at least twosubstrates, wherein the two substrates are electrically connected, butdo not require mechanical bonding.

A package provides electrical connection between the bond pads on thechip (or for example a multi-chip module) to a metal lead that can besoldered to a printed circuit board (or PCB). A package typicallycomprises a substrate and a cover. An Integrated Circuit (IC) substratemay refer to a silicon substrate with electrical circuits, typicallyCMOS circuits. A MEMS substrate provides mechanical support for the MEMSstructure(s). The MEMS structural layer is attached to the MEMSsubstrate. The MEMS substrate is also referred to as handle substrate orhandle wafer. In some embodiments, the handle substrate serves as a capto the MEMS structure.

Some embodiments may, for example, comprise one or more motion sensors.For example, an embodiment with an accelerometer, a gyroscope, and amagnetometer or other compass technology, which each provide ameasurement along three axes that are orthogonal relative to each other,may be referred to as a 9-axis device. In another embodiment three-axisaccelerometer and a three-axis gyroscope may be used to form a 6-axisdevice. Other embodiments may, for example, comprise an accelerometer,gyroscope, compass, and pressure sensor, and may be referred to as a10-axis device. Other embodiments may not include all the sensors or mayprovide measurements along one or more axes. Some or all of the sensorsmay be MEMS sensors. Some or all of the sensors may be incorporated in asensor processing unit along with a sensor processor and disposed in asingle semiconductor package.

For example, one or more sensors may, for example, be formed on a firstsubstrate. Various embodiments may, for example, include solid-statesensors and/or any other type of sensors. The electronic circuits insensor processing unit may, for example, receive measurement outputsfrom the one or more sensors. In various embodiments, the electroniccircuits process the sensor data. The electronic circuits may, forexample, be implemented on a second silicon substrate. In someembodiments, the first substrate may be vertically stacked, attached andelectrically connected to the second substrate in a single semiconductorchip, while in other embodiments, the first substrate may be disposedlaterally and electrically connected to the second substrate in a singlesemiconductor package, such as a single integrated circuit.

In an example embodiment, the first substrate is attached to the secondsubstrate through wafer bonding, as described in commonly owned U.S.Pat. No. 7,104,129, to simultaneously provide electrical connections andhermetically seal the MEMS devices. This fabrication techniqueadvantageously enables technology that allows for the design andmanufacture of high performance, multi-axis, inertial sensors in a verysmall and economical package. Integration at the wafer-level minimizesparasitic capacitances, allowing for improved signal-to-noise relativeto a discrete solution. Such integration at the wafer-level also enablesthe incorporation of a rich feature set which minimizes the need forexternal amplification.

Wear of Hearables by a User

FIG. 1 shows a diagram of a human user 100 wearing a pair of hearables110-1, 110-2 in the ears 104-1, 104-2 of the user 100, in accordancewith various aspects of the present disclosure. The wear location of ahearable 110 puts it in a near constant position and orientation withrespect to the head 101/ear 104 (104-1 and/or 104-2) of a user 100.Because of this, motion sensors of a hearable 110 can be used to acquireinformation about the movement of the head 101 of the user 100. Thesignals from the motion sensor comprise information about theorientation of the head and of the movement of the head. The head 101 ofthe user 100 includes the user’s cranium 102 and the user’s mandible 103(lower jaw). The wear location of the hearable makes it possible tomeasure the motion and/or orientation of the cranium 102 and themandible 103. The motion signal of the motion sensor may be acombination of the motion of the cranium and the motion of the mandible.Based on analysis of the motion signals, the motion and/or orientationof the cranium 102 and mandible 103 can be separated, as will be discussin more detail below. The motion sensor may also detect motion of thebody of the user, which may also be filtered out to determine the headmotion only. The motion and orientation of the cranium 102 and themandible 103 of the user 100 may be determined with respect to anexternal reference frame or may be determined with respect to eachother. Motions 120, 130, and 140 are related to the cranium 102, whilemotion 150 is related to the mandible 103. Motion 120 is an up/downnodding or pitch motion of head 101 and in particular of cranium 102.Motion 130 is a left/right twisting or yaw motion of head 101 and inparticular of cranium 102. Motion 140 is a left side to right sidetilting or roll motion of head 101 and in particular of cranium 102.Motion 150 is an up/down motion of mandible 103, such as might occurduring speaking or chewing with the mouth 105 of user 100. These andother motions of head 101, including vibratory motions, may be measuredby and acquired from motion sensors of hearables 110. Vibratory motionsmay also be measured by one or more microphones in the hearable. Thismay be a dedicated microphone, or a microphone that is used to performthe main function of the hearable.

Description of an Example Hearable

FIG. 2 illustrates the external configuration of an example hearable 110(e.g., 110-1, 110-2, etc), in accordance with various aspects of thepresent disclosure. This example shows only a possible configuration,formfactor, design and architecture. Many different configurations arepossible, varying from embodiments where most of the device is wornoutside the ear, to other embodiment that are completely worn inside theear. Although a single hearable 110 is shown, they are often worn inpairs. Hearable 110 comprises a wearable structure 211 which is ahousing for a plurality of components depicted in FIG. 2 and in FIG. 3 .By “wearable” what is meant is that the outer housing of the hearable isconfigured to couple with an ear 104 of a user 100 such that it can beworn in the coupled state without easily coming uncoupled from the ear104 unless the user 100 desires to remove the wearable structure 211.The coupling may include one or more of insertion into the ear canal orriding atop the ear. As depicted, wearable structure 211 includes aninside the ear portion 212 and an outside the ear portion 213. At leastsome, and as much as all, of inside the ear portion 212 is worn insidethe ear opening/ear canal of an ear 104 of user 100. At least some, andas much as all, of outside the ear portion 213 is worn outside of theear opening/ear canal of an ear 104 of user 100. When in use, at least aportion of hearable 110 is worn inside of the ear 104 of a user 100.Typically, this inside the ear portion 212 includes a speaker 216 or asound tube which is coupled with the speaker 216 of the hearable. Thespeaker supplies audible sound for user 100 to hear by converting anelectrical audio signal into a corresponding emitted acoustic signalwhich is audible to the user. Other configurations of a hearable 110 maylook different from what has been illustrated in FIG. 1 and FIG. 2and/or may couple to an ear 104 in a different manner than illustratedbut will still have a portion which resides in the ear canal/ear openingof a user 100.

As illustrated, a hearable 110 also includes one or more microphones217. A greater or lesser number of microphones 217, than depicted, maybe included in other embodiments. The microphones may be designed topick up the frequencies of the human hearable spectrum transmittedthrough the air or through bone conduction, for example, to pick upvibrations transmitted through the mandible or other bone structures.Microphones 217 may be disposed on inside the ear portion 212 (e.g.,microphones 217-1 and 217-2) and/or on outside the ear portion 213(e.g., microphones 217-3 and 217-N) of hearable 110. Each microphone 217may be a single microphone or a cluster of microphones, such as an arrayof three small microphones. Microphones 217-1, 217-2, or their like areconfigured to be disposed at least partially within the ear canal of auser 100 to acquire audio of the user 100 and to provide feedback basedon the sounds generated by speaker 216. Microphones 217-3, 217-N, ortheir like are configured to be disposed outside of the ear canal of auser 100 to acquire audio in the environment of the user 100. Beamforming using a group of microphones 217 may be employed to locate theposition of an audio source, such as a person speaking or the source ofa sound, relative to the head 101 of user 100. In some instances, suchbeamforming may be practiced with microphones of a pair of hearables 110which are disposed each of the ears 104 of a user 100.

As illustrated, a hearable 110 may also include one or more pressuresensors 219 (e.g., 219-1 and/or 219-2) which detect applied physicalforce. For example, pressure sensor 219-1, when included, may sensepressure from an ear canal on hearable 110 when it is installed inand/or residing in and ear 104 of user 100 and thus provide anindication of when hearable 110 is installed in the ear 104 of a user100. Pressure sensor 219-2, when included, may sense touch or squeezeinteractions of the fingers of a user 100 which provide inputs tocontrol the operation of hearable 110.

FIG. 3 shows a block diagram of components of an example hearable 110,in accordance with various aspects of the present disclosure.

As shown, example hearable 110 comprises a communications interface 305,a host processor 310, host memory 311, at least one speaker 216, one ormore microphones 217 (e.g., microphone 217-1, microphone 217-2,microphone 217-3, and/or microphone 217-N), and a sensor processing unit(SPU) 320. In some embodiments, hearable 110 may additionally includetransceiver 313 and a one or more pressure sensors 219 (e.g., pressuresensor 219-1 and/or pressure sensor 219-2). As depicted in FIG. 3 ,included components are communicatively coupled with one another, suchas, via communications interface 305. In one embodiment, as will befurther discussed, a hearable 110 may provide a health indicator 301 asan output.

In some embodiments, hearable 110 may be a self-contained device thatperforms its own operations independently of other electronic devices.However, in other embodiments, hearable 110 may function in conjunctionwith another electronic device such as personal computer, smartphone,smartwatch, tablet computer, another hearable 110, etc., which cancommunicate with hearable 110, e.g., via network connections. Hearable110 may, for example, be capable of communicating via a wired connectionusing any type of wire-based communication protocol (e.g., serialtransmissions, parallel transmissions, packet-based datacommunications), wireless connection (e.g., electromagnetic radiation,infrared radiation or other wireless technology), or a combination ofone or more wired connections and one or more wireless connections.

The host processor 310 may, for example, be configured to perform thevarious computations and operations involved with the general functionof hearable 110 (e.g., receiving audio from microphone 217-3, processingit, and supplying it to speaker 216). Host processor 310 can be one ormore microprocessors, central processing units (CPUs), DSPs, generalpurpose microprocessors, ASICs, ASIPs, FPGAs or other processors whichrun software programs or applications, which may be stored in hostmemory 311, associated with the general and conventional functions andcapabilities of hearable 110.

Communications interface 305 may be any suitable bus or interface, suchas a peripheral component interconnect express (PCIe) bus, a universalserial bus (USB), a universal asynchronous receiver/transmitter (UART)serial bus, a suitable advanced microcontroller bus architecture (AMBA)interface, an Inter-Integrated Circuit (I2C) bus, a serial digital inputoutput (SDIO) bus, or other equivalent. Communications interface 305 mayfacilitate communication between SPU 320 and one or more of hostprocessor 310, host memory 311, speaker 216, and/or microphone(s) 217.

Host memory 311 may comprise programs, modules, applications, or otherdata for use by host processor 310. In some embodiments, host memory 311may also hold information that that is received from or provided tosensor processing unit 320. Host memory 311 can be any suitable type ofmemory, including but not limited to electronic memory (e.g., read onlymemory (ROM), random access memory (RAM), or other electronic memory).

Transceiver 313, when included, may be one or more of a wired orwireless transceiver which facilitates receipt of data at hearable 110from an external transmission source and transmission of data fromhearable 110 to an external recipient. By way of example, and not oflimitation, in various embodiments, transceiver 313 comprises one ormore of: a cellular transceiver, a wireless local area networktransceiver (e.g., a transceiver compliant with one or more Institute ofElectrical and Electronics Engineers (IEEE) 802.11 specifications forwireless local area network communication), a wireless personal areanetwork transceiver (e.g., a transceiver compliant with one or more IEEE802.15 specifications (or the like) for wireless personal area networkcommunication), and a wired a serial transceiver (e.g., a universalserial bus for wired communication).

Speaker 216 may be, without limitation: a moving coil speaker, apiezoelectric speaker, or any other suitable type of speaker whichconverts an electrical audio signal into a corresponding emitted useraudible acoustic signal (i.e., a sound) which is capable of being heardby a user 100 who is wearing hearable 110 in an ear 104. In variousembodiments, speaker 216 may be capable of producing an emitted acousticsignal anywhere in the range between 20 Hz and 20 kHz. Other acousticranges are possible and anticipated. In some embodiments, a speaker 216may only be functional over a portion of this acoustic range such asbetween 20 Hz and 15 kHz. In some embodiments, more than one speaker 216may be included in hearable 110, and the speakers may have the same ordifferent acoustic ranges.

A microphone 217 (including microphones 217-1, 217-2, 217-3 and 217-N)may be any type of microphone which receives an audible acoustic signal(i.e., a sound) and converts it to a corresponding electrical audiosignal. A microphone 217 may comprise, without limitation, apiezoelectric microphone, a micro-electrical mechanical system (MEMS)microphone; an electrostatic microphone, or any other suitable type ofmicrophone.

A pressure sensor 219 (including pressure sensors 219-1 and 219-2) maybe any type of pressure sensor which detects physical pressure and/ortouch of a user and converts this sensed information into an electricalsignal. A microphone 217 may comprise, without limitation, a resistivepressure sensor, a strain gauge, a piezoelectric pressure sensor, acapacitive pressure sensor, an inductive pressure sensor, and the like.

SPU 320 comprises: a sensor processor 330; internal memory 340; one ormore motion sensors 350 (e.g., gyroscope 351, accelerometer 353,magnetometer 355 and/or other motion sensors such a barometric pressuresensor 357 which measures atmospheric pressure), and one or morefilter(s) 390. In some embodiments, SPU 320 may include one or morespeakers 360, one or more microphones 370, and/or one or more pressuresensors 380 (which are similar in operation to pressure sensors 219). Invarious embodiments, SPU 320 or a portion thereof, such as sensorprocessor 330, is communicatively coupled with host processor 310, hostmemory 311, and other components of hearable 110 through communicationsinterface 305 or other well-known means. SPU 320 may also comprise acommunications interface (not shown) similar to communications interface305 and used for communications among one or more components within SPU320.

Processor 330 can be one or more microprocessors, CPUs, DSPs, generalpurpose microprocessors, ASICs, ASIPs, FPGAs or other processors thatrun software programs, which may be stored in memory such as internalmemory 340 (or elsewhere), associated with the functions of SPU 320. Insome embodiments, one or more of the functions described as beingperformed by sensor processor 330 may be shared with or performed inwhole or in part by another processor of a hearable 110, such as hostprocessor 310.

Internal memory 340 can be any suitable type of memory, including butnot limited to electronic memory (e.g., read only memory (ROM), randomaccess memory (RAM), or other electronic memory). Internal memory 340may store algorithms, routines, or other instructions for instructingsensor processor 330 on the processing of data output by one or more ofthe motion sensors 350.

In some embodiments, internal memory 340 may store one or more modules342 which may be algorithms that execute on sensor processor 330 toperform a specific function. The modules 342 may be statisticalprocessing modules, activity detection modules, motion processingmodules (e.g., a head motion processing module, a jaw bone (mandible)vibration processing module, a jaw (mandible) movement module), audioprocessing modules (e.g., speech recognition module, an audiothresholding module, and a beam formation/sound direction determiningmodule), and/or decision-making modules.

In some embodiments, internal memory 340 may store a baseline model ofnormal temperature 343 which may be built over time from a plurality oftemperature measurements obtained by sensor processor 330 from one ormore temperature compensating circuits coupled with sensors such as oneor more microphones 217/370, and or one or more motion sensors 350(e.g., gyroscope 351, accelerometer 353, magnetometer 355, barometricpressure sensor 357 etc.). For example, as will be explained further inconjunction with FIG. 5 , one or more of such sensors may include atemperature sensor, used as a temperature compensating circuit, coupledwith or associated with the particular sensor and configured to measurea temperature in an environment of the sensor for use in temperaturecompensation of operation of the sensor. Such temperature compensationmay comprise adjusting the gain or the offset of the sensor based onfluctuations in temperature. Although depicted as a portion of sensorprocessing unit 320, in some embodiments, baseline model of normaltemperature 343 may be built and/or maintained in another location suchas in host memory 311 by host processor 310 or in a memory of a device(e.g., a cellular telephone or computing device) to which hearable 110is communicatively coupled.

Motion sensors 350, when included, may be implemented as MEMS-basedmotion sensors, including inertial sensors such as a gyroscope 351 oraccelerometer 353, an electromagnetic sensor such as a Hall effect orLorentz field magnetometer 355, and or a barometric pressure sensor 357(e.g., a capacitive MEMS sensor which can measure changes in height as achange in barometric pressure). In some embodiments, at least a portionof the motion sensors 350 may also, for example, be based on sensortechnology other than MEMS technology (e.g., CMOS technology, etc.). Asdesired, one or more of the motion sensors 350 may be configured toprovide raw data output measured along three orthogonal axes or anyequivalent structure. Motion sensor(s) 350 are communicatively coupledwith sensor processor 330 by a communications interface, bus, or otherwell-known communication means.

A speaker 360 when included, may be any type of speaker which convertsan electrical audio signal into a corresponding user audible emittedacoustic signal (i.e., a sound). In some embodiments, a speaker 360 maybe based on MEMS technology. In some embodiments, more than one speaker360 may be included, and the speakers may have the same or differentacoustic ranges.

A microphone 370, when included, may be any type of microphone whichreceives an acoustic signal (i.e., a sound) and converts it to acorresponding electrical audio signal. A microphone 370 may comprise,without limitation, a piezoelectric microphone, a micro-electricalmechanical system (MEMS) microphone; an electrostatic microphone, or anyother suitable type of microphone. In some embodiments, more than onemicrophone 370 may be included.

Filter(s) 390, when included, may be analog, digital, or somecombination thereof. Filters 390 may include one or more of: a finiteimpulse response (FIR) filter, a bandpass filter, a fast Fouriertransform (FFT) filter (FFT may also be performed as algorithmically bysensor processor 330). Other types of filters may additionally oralternatively be included in filters 390.

As discussed in conjunction with FIG. 3 , a variety of sensors may beincluded in a hearable 110. Generally speaking any of these sensors iscoupled with the wearable structure 211 and configured to senseinformation in an environment of the user 100. Some of these sensors aredisposed on the portion 212 of the wearable structure 211 which isconfigured to be disposed within the ear 104 of the user 100 when thehearable 110 is in use, while others of these sensors are disposed onthe portion 213 of the wearable structure 211 which is configured to bedisposed outside the ear 104 of the user 100 when the hearable 110 is inuse.

Operation of Hearable(s)

While hearables 110 are being worn by user 100, motion sensor(s) 350 ofthe hearables 110 may be used to pick up motion and vibrations of themandible 103 related to the motion and sound that is produced whenspeaking, chewing, or swallowing and/or the up/down motion 150 of themandible 103 when speaking, chewing, or swallowing. A single motionsensor 350 may be used to detect the vibrations and the slower motion ofthe mandible 103 or dedicated/specialized motion sensors 350 may beused. For example, one accelerometer 353 in a hearable 110-1 in a user’sleft ear 104-1 may be dedicated to detecting motion of the mandible 103(e.g., up/down motion 150 from talking/chewing), while anotheraccelerometer 353 in a hearable 110-2 in the user’s right ear 104-2 maybe dedicated to detecting and acquiring vibrations of the mandible 103which occur due to modulation by the voice of the user 100 when talking.Head motion data may also be collected on movements of the cranium 102,such as a tilting left/right motion 120, a rotating left/right motion130, and/or a tilting up/down motion 140. Analysis of cranium motiondata and mandible motion data can be separate or combined, and either orboth may be combined with the analysis of contemporaneous audio datacaptured by one or more microphones 217/370 of a hearable or pair ofhearables. Head motion data may comprise cranium motion data andmandible motion data. In some embodiments, the cranium motion data is ofinterest and may be separated from, or filtered out, of the head motiondata. In other embodiments, the mandible motion data is of interest andmay be separated from, or filtered out, of the head motion data. Themandible motion data may be further separated into, or filtered into,mandible movement data (e.g., rotation) and mandible vibration data. Thefiltering may be done using, for example, frequency filtering. Othermethods such as, for example, statistical feature analysis or machinelearning techniques may also be used for the extraction and separationof the motion signals of interest from the head motion data. Dependingon the health indicator to be determined, the cranium motion data and/ormandible motion data may be determined.

In some embodiments, speaking detection is performed by comparing themotion measured by the motion sensor (e.g., accelerometer 353) to one ormore thresholds. The threshold(s) may be a preset or it may becustomized for a particular user 100. If the amplitude of mandiblemotion data and/or mandible vibration is below the threshold(s) beingused, it may be deduced that the user 100 is not speaking. If theamplitude of mandible motion data and/or mandible vibration is at orabove the threshold(s) being used, it may be deduced that the user 100is speaking. Different thresholds may be used for mandible motion andmandible vibration to classify different activities, such as e.g.,speaking, eating, chewing, swallowing, teeth grinding, drinking.

In some embodiments, the speaking detection is performed by analyzingthe correlation between the motion data from a motion sensor (e.g.,accelerometer 353) and the audio data from one or more microphones217/370 for the same period of time. In other words, if the microphone217/370 detects sounds indicative of human speech, it should be analyzedif the human speech is caused by the user speaking. The microphonesignal related to human speech may be compared to the mandible motionand mandible vibration, and a correlation between the microphone signaland the motion signal may be determined over a period of time coveringthe human speech. If there is no or little correlation, then it isdetermined that the user 100 is not speaking. In this case the motionsensor 350 may have measured motion not related to the speaking, but forexample due to chewing or other jaw movement (which may have taken placeat the same time as the human speech from another source). Thecorrelation threshold to decide if the user is speaking may be adaptedto the user, the context, and/or the type and architecture of thehearable. The determination if the user is speaking may be only based onthe correlation between the motion signals and the microphone signal.Alternatively, analysis of the sound, such as e.g., frequency analysisor speech analysis, may also help determine if the sound is coming fromthe user, and this analysis may be combined with the speakingdetermination based on the motion signals. When it is determined thatthe user is not speaking, it may be determined that the user islistening.

In yet another embodiment, a frequency analysis may be used to separatedifferent activities, such as listening, speaking and/or chewing. Forexample, the motion data may be filtered centered around frequencies ofinterest. Different frequency bands may be used to pick up, and eitherdifferentiate between different activities or characterize motion datain a binary fashion e.g., speaking versus not speaking (anything whichdoes not correspond to speaking). Mandible vibration for an adult maleover age 20 is typically around 100 Hz when speaking and for an adultfemale is typically around 200 Hz when speaking. Likewise, mandiblevibration for any user is typically around 1-2 Hz for chewing andswallowing. Mandible motion data can be filtered around thesefrequencies, such as with bandpass filters and/or finite impulseresponse filters to determine when and differentiate between speaking,chewing/eating, and swallowing. The energy of the different frequencybands may be determined and compared to thresholds, which may be userdependent and/or context dependent. A time analysis or time thresholdmay also be used in the classification. For example, activities likespeaking or chewing normally have a duration of a few seconds, whileswallowing is of shorter duration. The time analysis may also be used tominimize false positives. For example, when a certain detection was madebased on the motion data, but because the duration was too short, or therepetition not enough, it could be the determined class, but rather anisolation motion event.

Audio data from one or more microphones 217/370 may also be filtered,such as with a bandpass filter which captures the range of frequenciesassociated with speaking (e.g., a bandpass filter which passes a band of90 to 250 Hz). The microphone(s) 217/370 may have a high bandwidth andare used to detect hearable sound spectrum and are not used to detectvibration. In some embodiments, this filtered audio data may be usedindependently of motion data to determine whether a user 100 is speakingor not speaking. In other embodiments, this filtered audio data may besynchronized in time with filtered motion data to determine whether bothsets of data indicate a person is speaking. Synchronization may beaccomplished in a variety of ways, including via time stamps collectedwith the capture of each type of data and via collecting and processingthe data substantially at the same time. In some embodiments, when thesynchronized motion data and audio data correlate, a user 100 isdetermined to be speaking otherwise they are determined not to bespeaking. In this case, if human speech, or other sounds, are detectedwhen the user is not speaking, it may be determined that the user islistening. An audio threshold may be used to remove sounds that have atoo low amplitude.

Any of the methods and or techniques known to the person skilled in theart may be used for the activity detection. In some embodiments, thevarious classification settings, such as e.g., motion thresholds orfrequency threshold, may be preset. The presets may be adaptive to theuser 100. In some embodiments, machine learning techniques may beapplied. In an initialization/learning stage, the user 100 may be askedto perform certain action, and the motion data may be analyzed to createthe classification rules. For example, custom thresholds may be createdby acquiring motion data during an initialization stage when the user100 is instructed to speak. While speaking mandible motion data and/ormandible vibration data (and a frequency range and threshold amplitudeand/or audio data (and a frequency range and threshold amplitude) whichare associated with speech by user 100 can be acquired by motion sensors350 of hearable 110, and one or more motion thresholds established.Similarly, (motion) thresholds, frequency setting, and other parametersmay be determined for other activities using initialization/learningstages. The machine learning may be applied for the user in question ormay be performed using crowd sourcing techniques. In the latter, thesetting for the user may be based on setting from other/similar users.These machine learning techniques would be known to the person skilledin the art and could be easily applied or adapted for the currentapplication.

Based on the analysis of mandible motion 150 and/or mandible vibrations,alone, or in combination with the audio data, it can be determined whenand for what length of time the user 100 is speaking and when and forwhat length of time the user 100 is merely hearing/listening. Thespeaking versus listening data may be analyzed to extract one or morehealth indicators. The term “health” may refer to social health, mentalor physical health. A health indicator may be useful for the monitoringof the health of elderly people. For example, a social health indicatormay give an indication of the social interactions of a user, and aphysical health indicator may be used as an indication of how well theuser can hear or follow a conversation. An example health indicator is aspeaking versus listening ratio. This ratio may be determined and thenused as a social health and/or mental health indicator for the user 100.This speaking/listening ratio may be used to derive a social healthindex/status. The speaking/listening ratio may be instantaneous orcumulative. By “instantaneous” it is not meant that the ratio must becalculated right way after acquisition of data, but rather that it isfor a small instant of time associated with the time period for whichdata is captured. This time period may be a few seconds, many minutes,or hours, or days. For example, an instantaneous health indicator maycover a one-week period. By cumulative, what is meant is that aplurality of the instances of speaking or not speaking are cumulated andthe ratio is calculated from a plurality of time periods during whichmotion data and/or audio data were analyzed to determine if the user 100was speaking or not speaking (e.g., hearing/listening). The cumulativehealth indicator may thus be a compilation of instantaneous healthindicators, which may be the same instantaneous health indicators fromdifferent points in time, or different health indicators. The time spanof “instantaneous” and “cumulative” may vary depending on the user, theapplication, and or the context. However, the use of these terms is toindicate that the “instantaneous” health indicator is based on analysisthat spans a shorter time than the analysis for the “cumulative” healthindicator. Correlation between the health indicators and theactivities/habits of the user may also exist. Different users havedifferent social activities, with different durations, occurrences, andrepetitions. In some embodiments, a speech recognition module may beused. In its simplest form, the speech recognition module can identifywhen a sound is representative of human speech, but can do no furtheranalysis. In some embodiments, the speech recognition module may be ableto analyze what is being said, or if things are being repeated.Repetition in speaking and/or listening may also be used as a healthindicator. For example, when someone’s hearing capacity declines, aperson speaking to the user often has to repeat his- or herself.

Another aspect that may be taken into account in the speaking/listeninganalysis, is how the user focuses on sound sources. For example, it maybe determined how the user moves the head towards a sound source. Usingthe microphones of the hearable, the direction of the sound source maybe determined, and then it may be analyzed how the user moves the headin the direction of the sound source. It is normal behavior for peopleto look in the direction of the sound source, for example, when in aconversion with someone, or when being addressed. Any change in behaviormay be used as a health indicator, for example, of decreased hearing ormental capacity. The angle of direction of the sound source with respectto the user may be determined, and then compared with the rotation angleof the head of the user. This analysis may be triggered at theinitiating of a sound source, for example when someone starts speakingor specifically addressed the user.

In some embodiments, when the source of a sound is located and a user isnot oriented toward the source a triggering mechanism such as hapticfeedback (e.g., buzzing of the hearable 110 in one ear 104) or audiblefeedback (e.g., “look left,” “look up” “look right”) may be provided todirect head orientation toward a sound source. Consider someone crossingthe road and ambulance or car is approaching with a honk, a categoricalsound detection and no response from the user may force a triggeringmechanism to draw the attention of user towards sound source.

In some embodiments when a user 100 is classified as listening, voicerecognition may be utilized to determine if the speaker repeats words,phrases, or sentences to user 100. Detection of repetition of samesentence from the speaker who is trying to communicate with hearablewearing user 100 can provide a health indicator related to robustness ofcognition and/or hearing of user 100. Additionally, the repetition maybe a sign that the hearable 110 is not working properly and that anaction should be taken, such as: performing an automated self-check ofhearable 110 or providing an audible instruction to user 100 toreposition hearable 110.

When a user 100 is classified as hearing/listening, an audio analysismay be performed upon audio data captured with a microphone 217/370 todetermine whether the user 100 is listening to other people speaking orto recorded audio (e.g., radio, television, or the like). The socialhealth index or speaking/listening ratio may comprise listening to allaudio, or only to specific types of sources, such as e.g., other peoplespeaking. The speaking/listening ratio may also be combined with otherindicators of social health to determine a social health index. Thespeaking/listening ratio may be compared with a benchmark ratio. In someembodiments, classification may be based on crowdsourcing techniques todetermine the distribution of ratios of different people and what aratio means for the social health of a user 100 compared to other users.This ratio and/or the social health index can be monitored over time tosee if there are any changes. For example, the speaking/listening ratiomay be compared with a previously determined ratio (e.g., month overmonth, year over year, or the like) for the same user 100 to determineif there has been a change (i.e., the user 100 is speaking less and thusis not as engaged as they once were). For people with health issues, forexample elderly people with Alzheimer disease, monitoring of thespeaking/listening ratio and/or the social health index over time may beused to monitor gradual or sudden changes of the health of the user 100.

The context of the user 100 may also be determined using sensors in thehearables 110, or in any other device in communication with thehearables (e.g., a smartphone, smart watch, computer). The determinedcontext may then be used to set or adjust the parameters of the system.For example, it may be determined if the user 100 is in a noisyenvironment with many people, such as e.g., a restaurant, or having aquite private conversation. Or it may be determined that the user 100 isrunning, walking, or performing some other type of activity which doesnot lend itself to speaking. Depending upon the context and/or activityof the user 100, the classification of what constitutes speaking may beadjusted. In one embodiment, speaking determination may simply be turnedoff in certain contexts (e.g., noisy environment) or during certainactivities (e.g., running). In another embodiment, such as a noisyenvironment, audio data may be disregarded, and classification may bemade using motion data only. In yet another embodiment, such as a noisyenvironment, amplitude thresholds may be raised as a user 100 may beexpected to speak louder in the noisy environment. The activities of theuser may also affect the head motions and vibrations, and therefore,based on the determined context/activity, the parameters/settings of themotion analysis may be adapted. This may minimize the impact of themotion of the body on the detected motion of the head.

In some embodiments, when motion is detected by the motion sensors 350,but it is determined that the user 100 is not speaking, the motion datamay be related to the user 100, eating, chewing, or drinking. Theseactivities may be further analyzed to determine whether e.g., the user100 is eating too fast, not chewing food enough, or speaking whileeating. This can be used to detect (bad) habits and may be used to givefeedback and/or notifications to the user 100. The total eating/chewingtime may also be determined and used for analysis. This may be useful toa user 100 who should eat slowly or not too much because of medicalconditions. It can also be determined if a user 100 eats when theyshould eat, if they have a regular pattern, or if they skip a meal, orhave snacks at inappropriate times. Using the mandible motion andmandible vibration, alone or in combination with audio data, the type offood the user 100 is eating may also be classified such as e.g., soft,hard, or chewy.

Cranium motion 120, 130, and 140 and/or mandible motion 150 may also beused to detect if a user 100 takes a medicine they need to take (e.g.,by monitoring swallowing) at the appropriate times. Movement and/orposition of the mandible 103 and the cranium 102 may both be used in theanalysis. For example, when swallowing pills, a user 100 normally tiltshis or her cranium back while swallowing, which is easily detectable.

The discussion above shows examples of the calculation of healthindicators through the use of motion sensors incorporated intohearables. Many different variations and other health indicators may beeasily envisioned. They key is the analysis of the head motion, whichmay include determining motion and/or orientation of the cranium, andmotion and/or vibrations of the mandible. One the different motions havebeen separated, statistical feature analysis and machine learningtechniques may be applied to derive the desired health indicators.

Example Activity Classification Operations

FIG. 4 illustrates a flow diagram 400 of the operation of a hearable 110to classify an activity of the head 101 of a user 100, in accordancewith various aspects of the present disclosure. In some embodiments, notall blocks and/or procedures illustrated are accomplished. That is, somemay be omitted or skipped. In some embodiments, one or more blocks orprocedures that are not depicted may additionally or alternatively beincluded. Starting at the upper left corner a three-axis accelerometer353 of a hearable 110 acquires head motion data 401, which may be timestamped. At the substantially the same time, a three-gyroscope 351 ofhearable 110 acquires head motion data 401 which may be time stamped.The head motion data 401 may then be analyzed, processed and/orfiltered, to extract the motion data of interest, such as e.g., craniummotion data, mandible motion data 407 (regarding mandible movementand/or vibration). For example, as shown in FIG. 4 , head motion data401 from accelerometer 353 and gyroscope 351 are provided to FIR filter491, which filters in the 100 to 200 Hz range to determine if mandiblevibrations 160 associated with speaking by user 100 are present. Headmotion data from accelerometer 353 and gyroscope 351 are also providedto FIR filter 492, which filters in the 1 to 5 Hz to determine ifmandible motion 150 associated with chewing by user 100 are present. Atsubstantially the same time as the acquisition of the head motion data401, one or more microphones 217 / 370 of the hearable 110 also acquireaudio data 408 which may be time stamped and provided to bandpass filter494 where frequencies between 90 and 250 Hz are permitted to pass, asthis frequency range is associated with fundamental frequencies of mostadult vocal cords, and these frequencies would occur if user 100 werespeaking. FIR filters 491 and 492 and bandpass filter 494 are examplesof filters 390 which may be incorporated into sensor processing unit320.

To eliminate noise from other sources, two FIR filters (491 and 492) areused due to the frequencies of speech activity and mastication differgreatly in bandwidth. Further noise isolation can be achieved by summingthe spectral frequency on the fact that the signal of interest will bein the same spectral range as compared to noise which would have aspread. Accordingly, as depicted, outputs from FIR filter 491 and FIRfilter 492 are summed and this mandible motion data 407 is provided asone input to silence detection module 410. A second input to silencedetection module 410 is audio data 408 (which has been filtered),provided from the output of bandpass filter 494.

Silence detection module 410 may be stored in memory 340 and operated asan algorithm on sensor processor 330. If the head motion data 401 andaudio data 408 are all time stamped, they may be easily synchronizedbefore filtering and upon receipt as inputs to silence detection module410. In some embodiments, the head motion data 401 and audio data 408are acquired and processed at the same time with inherentsynchronization. Silence detection module 410 separates voiced andsilence segments in the stream of inputs which it receives. By “silence”what is meant is that there is no signal of interest in the data streamfor a period of time. The root mean squared (RMS) energy of the signalfrom microphones 217/370 over short duration is beneficial in segmentingthe portions of filtered motion data and filtered audio data whichinclude speech by the user. The separation effected by silence detectionmodule 410 results in maximizing information associated with speechbefore processing it further. The output of silence detection module410, is a synchronized data stream 415 and is provided to featureextractor module 420. It should be appreciated that in some embodiments,silence detection module 410 may be omitted and a synchronized datastream 415 may be created from mandible motion data 407 and audio data408 and then provided as an input to feature extractor module 420.

Feature extractor module 420 extracts one or more spectral and/ortemporal features from the signal received as an input. Featureextractor module 420 may operate as a module on sensor processor 330. Ashort time Fourier transform (STFT) is applied to the summed signal fromthe gyroscope 351 and the accelerometer 353 to obtain time dependent andspectral features of the signal. In addition to spectral features, oneor more temporal features such as mean, variance, skewness, kurtosis,and entropy may be determined. For example, entropy of a signal whichquantifies the randomness in distribution of the signal may be useful incharacterizing stochastic process. Any other features of interest may bedetermined, and these features of interest depend on the healthindicator to be determined.

A fast Fourier transform (FFT) 493 may be used to similarly processaudio data acquired from microphones 217 / 370. Sensor processor 330 mayperform the FFT. This transformed audio data then has its featuresextracted by feature extractor module 405 which extracts features suchas peak frequency, center frequency, skewness, and kurtosis and suppliesthese features to machine learning module 430.

Machine learning module 430 receives features which were extracted bythe feature extractor module 420 and feature extractor module 405.Sensor processor 330 may operate machine learning module 430 as analgorithm. Machine learning module 430 applies one or more machinelearning techniques such as Support Vector Machine (SVM), Hidden MarkovModel (HMM), or Naive Bays with Kernel Density Estimator to detectcontest of speaking, listening, or mastication. The output 460 ofmachine learning module 430 is a classification of the activity of thehead 101 of user 100 as one or more of: listening, speaking, eating,drinking, chewing, swallowing, and/or teeth grinding. It should beappreciated machine learning module 430 may be configured to classify agreater or lesser number of activities and may classify other activitiesof a user’s head 101, such as sleeping, snoring, sneezing, and/orcoughing.

In some embodiments, audio data from microphones 217 / 370 can also besupplied to one or more modules 342, such as speech recognition module443, thresholding module 444, and or beam formation/sound directionmodule 445. Speech recognition module 443 recognizes speech in audiodata to determine if user 100 is speaking or someone is speaking to user100. Thresholding module 444 adjusts speaker and listener amplitudeswhich are used as thresholds for whether or not speech is occurring.These thresholds may be adjusted based upon environmental noise andother factors such as overall activity of a user (e.g., stationary,walking, running, driving, etc.). Beam formation/sound direction module445 uses signals from a plurality of microphones to form a beam todetect the direction from which a source of sound emanates relative touser 100. Triangulation module 446 uses signals from a plurality ofmicrophones in a known arrangement with respect to one another totriangulate the direction/location from which a source of sound emanatesrelative to user 100. If motion of user 100 indicates turning toward ortracking the source of the sound, there is a high likelihood that user100 is listening. As depicted, outputs from module(s) 342 are providedfor use in a voting scheme 450 which fuses the outputs of speech,thresholding, and beam formation to improve the accuracy of the contextof the environment of user 100. This context information may be suppliedto machine learning module 430 and/or used to validate user activityclassification outputs 460 which are made by machine learning module430. In some embodiments, voting scheme 450 may be eliminated andoutputs from modules 342 are provided as additional input to machinelearning module 430.

FIG. 5 illustrates a block diagram of a sensor (in this example, amicrophone 217) with a temperature compensating circuit (shown astemperature sensor 519), in accordance with various aspects of thepresent disclosure. The depiction of microphone 217 in FIG. 5 is shownby way of example and not of limitation and is meant to show how atemperature sensor 519 may be included as a component of one of thesensors (e.g., 217, 370, 350) described herein to compensate/adjust theoperation of the sensor based on the temperature in the immediatevicinity of the sensor. Microphone 217 as depicted in FIG. 5 includes anacoustic sensor 517 and a temperature sensor 519 (in the form of atemperature compensating circuit). In some embodiments, microphone 217may include its own processor 510 (such as a DSP) and a memory 511coupled thereto. In some embodiments, a processor and memory may beexternal to and communicatively coupled with microphone 217 (e.g., hostprocessor 310 and host memory 311). In typical and conventional usetemperature sensor 519 may be implemented as temperature compensatingcircuit which provides an output to control and adjust (i.e.,compensate) the operation of microphone 217 based on the temperature inthe vicinity of the microphone. For example, the gain of microphone 217may be adjusted based on a change in temperature. Other sensorsdiscussed herein may similarly employ a temperature sensor in the formof temperature compensating circuit to adjust their operation dependingon the temperature in the immediate vicinity of where the sensor islocated. Some of the embodiments herein, repurpose measurements of oneor more such existing temperature sensors 519 to also detect ambienttemperature and/or user temperature, thus obviating the need for atemperature sensor dedicated for these purposes.

Acoustic sensor 517 includes electrical and/or mechanical components(such as a piezo sensor or other suitable sensor) for acoustic sensing.

Processor 510, when included, is operable to compensate the output ofthe acoustic sensor 517 according to the output of the temperaturesensor 519.

Memory 511, when included, provides storage for acoustic data andtemperature data.

Output 501 may include an electrical representation of a measuredacoustic signal and/or an electrical measure of temperature.

In some embodiments, temperature sensor 519 includes circuitry and insome instances mechanical components that provide an output thatcorrelates to a measured temperature. Temperature sensor 519 is employedas a temperature compensating circuit. Such temperature compensatingcircuits are known in the art and are often implemented withtransistors, resistors, MEMS components, and/or other components.Further in some embodiments a temperature compensating circuit maymeasure the actual temperature, or a proxy thereof which can beconverted to the actual temperature (such as by processor 510), in theenvironment of the sensor which it is used for compensating. In suchembodiments, an output of this temperature or proxy for temperature(e.g., a change in resistance, current, voltage, etc.) may be acquiredand provided to a processor (e.g., processor 510, sensor processor 330,host processor 310, or another processor that is communicatively coupledwith hearable 110). Outputs from a temperature compensating circuit(which is disposed on the portion 212 of the wearable structure 211 thatis configured to be disposed within an ear 104 of the user 100 (when inuse)), may be used to build a baseline model 343 of the normaltemperature of the user. For example, the baseline temperature model maybe a running average of a certain number of recent measurements such asthe last two weeks of measurements.

Example Method of Hearable Operation

FIGS. 6 and 7A-7C illustrate flow diagram 600 and 700 of example methodsof hearable use, in accordance with various aspects of the presentdisclosure. Procedures of these example methods will be described withreference to elements and/or components of one or more of FIGS. 1-5 . Itis appreciated that in some embodiments, the procedures may be performedin a different order than described, that some of the describedprocedures may not be performed, and/or that one or more additionalprocedures to those described may be performed. Flow diagrams 600 and/or700 include some procedures that, in various embodiments, are carriedout by one or more processors (e.g., processor 330, host processor 310,or the like) under the control of computer-readable andcomputer-executable instructions that are stored on non-transitorycomputer-readable storage media (e.g., host memory 311, internal memory340, or the like). It is further appreciated that one or more proceduresdescribed in flow diagrams 600 and/or may be implemented in hardware, ora combination of hardware with firmware and/or software.

With reference to FIG. 6 , at procedure 610 of flow diagram 600, invarious embodiments, a sensor processor 330 of a hearable 110 acquiresaudio data from a microphone (e.g., a microphone 217 or 370) of ahearable 110, while the hearable 110 is disposed at least partiallywithin an ear 104 of a user 100 of the hearable 110. The audio data maybe filtered, such as with a bandpass filter centered around a frequencyassociated with vibrations caused by a user 100 speaking. For example,the audio filter may pass a band between 90 Hz and 250 Hz in oneembodiment. The center frequency bandpass filter and width of the bandpassed may be preset or may be determined for a user 100 during aninitialization period.

With continued reference to FIG. 6 , at procedure 620 of flow diagram600, in various embodiments, the sensor processor 330 of the hearable110 acquires head motion data from at least one motion sensor 350 of thehearable 110. The head motion data describes motions of a head 101 ofthe user 100. The head 101 comprises a cranium 102 and a mandible 103.The head motion data (e.g., 401) is acquired while the hearable 110 isdisposed at least partially within the ear 104 of the user 100. The headmotion data comprises cranium motion data (e.g., data describing craniummotions such as motion 120, motion, 130, and motion 140) and mandiblemotion data (e.g., mandible motion data 407 which describes mandiblemovement 150 and mandible vibration 160).

With continued reference to FIG. 6 , at procedure 630 of flow diagram600, in various embodiments, the sensor processor 330 separates themandible motion data from the head motion data. The mandible motion dataincludes up/down motion 150 and vibration 160. Filtering such as FIRfiltering or bandpass filtering may be used to determine the vibrations160 and/or motions 150 and separate mandible motion data from theoverall head motion data. For example, a separate filter may be used oneach of the frequencies associated with speaking and chewing.

With continued reference to FIG. 6 , at procedure 640 of flow diagram600, in various embodiments, the sensor processor 330 synchronizes twoor more of the cranium motion data, mandible motion data, and audio datainto a synchronized data stream. For example, the mandible motion dataand the audio data may be synchronized into a synchronized data stream(e.g., 415). The synchronizing may be accomplished using time stampswhich are associated with the different data during the capture of thedata. Synchronizing may also include synchronizing data captured by twoseparate hearables 110. This may comprise synchronizing clocks of thetwo hearables or determining an offset between the clocks of the twohearables. The synchronizing may also be accomplished by capturing thesources of the data substantially at the same time and processing thesources of the data at the same time. The synchronization accuracyneeded, in some embodiments, may be on the order of a second, but may bea shorter or longer span of time in other embodiments. The requiredsynchronization may depend on the health indicator to be determined andmay, as such, be adapted to the application.

With continued reference to FIG. 6 , at procedure 650 of flow diagram600, in various embodiments, the sensor processor 330 classifies anactivity of the head 101 during a portion of the synchronized datastream. In some embodiments, the activity may be classified as one ormore of listening, speaking, eating, drinking, chewing, swallowing, andteeth grinding. Based on the filtering of the motion data, the motiondata alone may be used to determine whether the user 100 is speaking ornot speaking. For example, if a filtered frequency range of mandiblevibration 160 associated with speaking exceeds an amplitude threshold,the user 100 is classified as speaking. Likewise, the filtered audiodata may be used alone to classify whether the user 100 is speaking ornot speaking. For example, if a filtered frequency range of audio dataassociated with speaking exceeds an amplitude threshold, the user 100 isclassified as speaking. In some embodiments, both the filtered audiodata and the filtered mandible motion for a synchronized period of timeare used together to determine if a user 100 is speaking or notspeaking. For example, if both the audio data and motion data exceedthresholds associated with speaking, the user 100 is classified asspeaking. If not, the user is classified as not speaking (e.g., aslistening). The different activities that can be determined depend onthe quality and accuracy of the motion data and the applied processingand filtering, and this may depend on the user, the hearable, and/or thecontext. The activities that can be reliably determined may be decidedbased on the machine learning.

In some embodiments, the classifying is adjusted based on context of anoverall activity being performed by the user 100. The context of theuser 100 may also be determined using sensors in the hearables 110, orin any other device in communication with the hearables (e.g., asmartphone, smart watch, computer). For example, it may be determined ifthe user 100 is in a noisy environment with many people, such as e.g., arestaurant, or having a quite private conversation. Or it may bedetermined that the user 100 is running, walking, or performing someother type of activity which does not lend itself to speaking. Dependingupon the context and/or activity of the user 100, the classification ofwhat constitutes speaking may be adjusted. In one embodiment, speakingdetermination may simply be turned off in certain contexts (e.g., noisyenvironment) or during certain activities (e.g., running). In anotherembodiment, such as a noisy environment, audio data may be disregarded,and classification may be made using motion data only. In yet anotherembodiment, such as a noisy environment, amplitude thresholds may beraised as a user 100 may be expected to speak louder in the noisyenvironment.

With continued reference to FIG. 6 , at procedure 660 of flow diagram600, in various embodiments, the sensor processor 330 generates a healthindicator for the user 100 based on the activity and the synchronizeddata stream.

In some embodiments, the health indicator may be an instantaneous healthindicator associated with the portion of the synchronized data stream.That is, it may be calculated immediately or at some other time for theinstance of time represented by the portion of the synchronized datastream. For example, this may comprise filtering the portion of thesynchronized data stream to determine a first amount of time attributedto speaking by the user and a second amount of time attributed tolistening by the user, and then determining a ratio between the firstamount of time and the second amount of time. As previously discussed,an instantaneous health indicator is for a single time period or ashorter interval than a cumulative health indicator.

In some embodiments, the health indicator may be a cumulative healthindicator associated with a plurality of instances of the activity bythe user. For example, a plurality of instances of speaking or notspeaking. The cumulative health indicator may indicate aspeaking/listening ratio over a period than an instantaneous healthindicator and/or may comprise a cumulation of the data of two or moreinstantaneous health indicators.

FIGS. 7A-7C illustrate a flow diagram 700 of an example method ofhearable use to generate a user health indicator 301 based on usertemperature, in accordance with various aspects of the presentdisclosure.

With reference to FIG. 7A, at procedure 710 of flow diagram 700, invarious embodiments, temperature data in a form of a plurality oftemperature measurements captured from a temperature compensatingcircuit 519 while a portion of the wearable structure 211 is disposedwithin the ear of a user 100 of a hearable 110. This temperature data ismeasured over a period of time such as minutes, hours, weeks, or more.The temperature data is provided to or acquired by a processor (e.g.,processor 510, sensor processor 330, host processor 310, or anotherprocessor that is communicatively coupled with hearable 110). Thetemperature compensating circuit 519 is coupled with a sensor (e.g.,microphone 217, microphone 370, a motion sensor 350 (gyroscope 351,accelerometer 353, magnetometer 355, and/or barometric pressure sensor357), and/or pressure sensor 219/380) of the hearable 110 and configuredto measure temperature in an environment of the sensor for use intemperature compensation of operation of the sensor. The temperaturecompensating circuit 519 is disposed on the portion 212 (e.g., on thesurface or within the interior space formed by that surface) of thewearable structure 211 of the hearable 110 which is configured to bedisposed within the ear 104 of the user 100.

With continued reference to FIG. 7A, at procedure 720 of flow diagram700, in various embodiments, a baseline model of normal temperature 343for the user 100 is built, by the processor, from the plurality oftemperature measurements. This baseline model may be stored in a memorythat is associated with or communicatively coupled with the processor.For example, the baseline model 343 may be an average of a plurality oftemperature measurements taken over a period of time such as overminutes, hours, days, weeks, or longer. In another embodiment, thebaseline model may be time-dependent for example, based on a nominal28-day (or other number of days) cycle, based on a circadian rhythm(e.g., there may be one baseline model for sleep times and anotherbaseline model for awake times). After a suitable period of time and asuitable number of measurements, the baseline model provides an accurateestimate of what the normal temperature is for a user of hearable 110.In some embodiments, when a user 100 is wearing a first hearable 110-1in a first ear 104-1 and a second hearable 110-2 in a second ear 104-2,similar temperature data may be measured bilaterally and averagedtogether or otherwise incorporated in the baseline temperature model343.

With continued reference to FIG. 7A, at procedure 730 of flow diagram700, in various embodiments, a temperature measurement acquired from thetemperature compensating circuit 519 is then compared, by the processor,to the baseline model of normal temperature 519 for the user. In someembodiments, when a user 100 is wearing a first hearable 110-1 in afirst ear 104-1 and a second hearable 110-2 in a second ear 104-2,similar temperature measurements may be acquired bilaterally atsubstantially the same time (e.g., within a few milliseconds to a fewseconds) and averaged together for comparison to the baselinetemperature model 343 or compared to one another for validation prior tocomparison of one or both to the baseline temperature model 343 atprocedure 730.

With continued reference to FIG. 7A, at procedure 740 of flow diagram700, in various embodiments, responsive to the comparison showing adeviation beyond a preset threshold from the baseline model, theprocessor generates a health indicator 301 for the user based on thedeviation. The health indicator 301 is used to monitor an aspect ofhealth of the user 100. For example, if the deviation is beyond athreshold in a number of degrees or a percentage from the normaltemperature of the user, an appropriate health indicator 301 isgenerated. In one example, when a normal temperature for a user is 98°F., a deviation measured at 2.5 or more degrees above this may beassociated with a fever and the health indicator 301 may alert the user100 that they likely have a fever. In one example, when a normaltemperature for a biologically female user 100 is 98° F., a deviationmeasured at 0.8 to 1.2 degrees above this at a certain time of day maybe associated with the user 100 experiencing ovulation and the healthindicator 301 may alert the user 100 that they are likely ovulating. Inone example, when a normal temperature for a user is 98° F., a deviationmeasured at 5 degrees or more above this may be associated withexperiencing heat stroke and the health indicator 301 may alert the user100 that they are experiencing heat stress and/or likely are in dangerof heat stroke. In one example, when a normal temperature for a user is98° F., a deviation measured at 3 degrees or more below this may beassociated with experiencing hypothermia and the health indicator 301may alert the user 100 that they are dangerously cold and/or likely arein danger of hypothermia.

In some embodiments, the processor generates an audible report of thehealth indicator 301 (e.g., “Your temperature is 102° F., and you mayhave a fever”) which is output to the user 100 such as via a speaker 216of a hearable 110.

In some embodiments, the processor generates a report of the healthindicator 301 (e.g., “Your temperature is 99° F., and you may beovulating”) which is output visibly to the user 100 such as via adisplay of a smartphone or computer to which hearable 110 iscommunicatively coupled and which has been selected by the user 100 fordisplay of such health indicators 301.

With reference to FIG. 7 , at procedure 750 of flow diagram 700, invarious embodiments the method as described in 710-740 furthercomprises, the processor acquiring a second temperature measurement froma second temperature compensating circuit. The second temperaturecompensating circuit (e.g., another circuit similar to temperaturesensor 519) is coupled with a second sensor (e.g., microphone 217,microphone 370, a motion sensor 350 (gyroscope 351, accelerometer 353,magnetometer 355, and/or barometric pressure sensor 357), and/orpressure sensor 219) of the hearable 110 and configured to measuretemperature in an environment of the second sensor for use intemperature compensation of operation of the second sensor. The secondsensor is a different sensor than the sensor described in procedure 710and is disposed on a second portion 213 of the wearable structure 211 ofhearable 110. In some embodiments, the second temperature measurement isacquired contemporaneously (e.g., within a few milliseconds to a fewseconds) with acquisition of the temperature measurement that wasdescribed in procedure 710. The second temperature compensating circuitis also disposed on (e.g., on the surface or within the interior spaceformed by that surface) of the second portion 213 of the wearablestructure 211 of hearable 110, where this second portion 213 isconfigured to be disposed outside the ear 104 of the user 100 when thehearable 110 is in use by the user 100.

With continued reference to FIG. 7B at procedure 760 of flow diagram700, in various embodiments, the processor computes a temperature of theuser based on the temperature measurement and the second temperaturemeasurement. For example, the processor may compare the temperaturemeasurement with an ambient temperature represented by the secondtemperature measurement and correlate the two temperatures to make amore accurate estimate of the user’s temperature than might be made fromjust the temperature measurement described in procedure 710. Forexample, if the user is in 45° F. ambient temperature, the hearable 110may be cooler than normal body temperature and thus impact thetemperature measuring described in procedure 710. In some embodiments, alookup table, graph, equation, or other relationship may be used tocompare the temperature measurement of procedure 710 to the secondtemperature measurement and then slightly increase or decrease theuser’s temperature as measured by the temperature measurement ofprocedure 710. This adjusted temperature is referred to as a “computedtemperature.”

With continued reference to FIG. 7B, in various embodiments, theprocessor generates a health indicator 301 for the user based on thedeviation and based further on an ambient temperature represented by thesecond temperature measurement. In this manner, when a user 100experiences an elevated temperature represented by the first measurementof procedure 710 and the second measurement describes a high ambienttemperature that exceeds a preset threshold (e.g., over 90° F. in oneexample) then a fever may be ruled out as a health indicator when adeviation from the baseline temperature model 343 correlates to bothexperiencing a fever and experiencing heat stress. Similarly, anotification regarding hypothermia may be made when an ambienttemperature is below a preset threshold (e.g., below 20° F.) and theuser’s measured or computed is decreasing at a rate that satisfies apreset threshold (e.g., 1 degree Fahrenheit per hour). It should beappreciated that a variety of ambient temperature thresholds may besimilarly related to rates of increase or decrease in measured orcomputed user temperature which trigger reporting of a user healthindicator 301.

With continued reference to FIG. 7B, at procedure 770 of flow diagram700, in various embodiments, a computed temperature measurement is thencompared, by the processor, to the baseline model of normal temperature343 for the user 100.

With continued reference to FIG. 7B, at procedure 780 of flow diagram700, in various embodiments, responsive to the comparison of thecomputed temperature showing a second deviation beyond the presetthreshold from the baseline model, the processor generates the healthindicator 301 for the user 100 based on the second deviation. Aspreviously discussed, this health indicator 301 may be audibly orvisibly provided to the user.

With reference to FIG. 7C, at procedure 790 of flow diagram 700, invarious embodiments the method as described in 710-740 furthercomprises, reporting, by the processor, the health indicator 301 to adevice selected by the user. For example, the health indicator 301 maybe generated by or provided to a smartphone or a computer (e.g., atablet computer, a notebook computer, etc.), as selected by user 100,which is communicatively coupled with hearable 110 and output as audioby a speaker of the selected device or as video, text, and/or image by adisplay of the selected device.

Conclusion

The examples set forth herein were presented in order to best explain,to describe particular applications, and to thereby enable those skilledin the art to make and use embodiments of the described examples.However, those skilled in the art will recognize that the foregoingdescription and examples have been presented for the purposes ofillustration and example only. The description as set forth is notintended to be exhaustive or to limit the embodiments to the preciseform disclosed. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims.

Reference throughout this document to “one embodiment,” “certainembodiments,” “an embodiment,” “various embodiments,” “someembodiments,” or similar term means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment. Thus, the appearances of suchphrases in various places throughout this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics of any embodimentmay be combined in any suitable manner with one or more other features,structures, or characteristics of one or more other embodiments withoutlimitation.

What is claimed is:
 1. A hearable comprising: a wearable structure at least a portion of which is configured to be disposed within an ear of a user of the hearable when the hearable is in use by the user; a speaker coupled with the wearable structure and configured to convert an electrical audio signal into a corresponding emitted acoustic signal which is audible to the user; a sensor coupled with the wearable structure and configured to sense information in an environment of the user, wherein the sensor is disposed on the portion of the wearable structure which is configured to be disposed within the ear of the user; a temperature compensating circuit coupled with the sensor and configured to measure a temperature in an environment of the sensor for use in temperature compensation of operation of the sensor, wherein the temperature compensating circuit is disposed on the portion of the wearable structure which is configured to be disposed within the ear of the user; and a sensor processing unit communicatively coupled with the sensor, the speaker, and the temperature compensating circuit, wherein the sensor processing unit is configured to: acquire temperature data in a form of a plurality of temperature measurements captured from the temperature compensating circuit while the portion of the wearable structure is disposed within the ear of the user; build a baseline model of normal temperature for the user from the plurality of temperature measurements; compare a temperature measurement acquired from the temperature compensating circuit to the baseline model of normal temperature for the user; and responsive to the comparison showing a deviation beyond a preset threshold from the baseline model, generate a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user.
 2. The hearable of claim 1, further comprising: a second sensor coupled with the wearable structure and configured to sense second information in the environment of the user, wherein the second sensor is disposed on a second portion of the wearable structure which is configured to be disposed outside the ear of the user when the hearable is in use by the user; a second temperature compensating circuit coupled with the second sensor and configured to measure a second temperature in an environment of the second sensor for use in temperature compensation of operation of the second sensor, wherein the second temperature compensating circuit is disposed on the second portion of the wearable structure; and wherein the second sensor and the second temperature compensating circuit are coupled with the sensor processing unit, and the sensor processing unit is further configured to: acquire a second temperature measurement from the second temperature compensating circuit, wherein the second temperature measurement is acquired contemporaneously with acquisition of the temperature measurement; compute a temperature of the user based on the temperature measurement and the second temperature measurement; compare the computed temperature of the user to the baseline model of normal temperature for the user; and responsive to the comparison of the computed temperature showing a second deviation beyond the preset threshold from the baseline model, generate the health indicator for the user based on the second deviation.
 3. The hearable of claim 1, wherein the sensor processing unit is further configured to: report the health indicator to a device selected by the user.
 4. The hearable of claim 1, wherein the sensor processing unit is further configured to: generate an audible report of the health indicator which is output by the speaker of the hearable.
 5. The hearable of claim 1, wherein the health indicator comprises: an indicator of the user having a fever.
 6. The hearable of claim 1, wherein the health indicator comprises: an indicator of the user experiencing ovulation.
 7. The hearable of claim 1, wherein the health indicator comprises: an indicator of the user experiencing heat stress.
 8. The hearable of claim 1, wherein the health indicator comprises: an indicator of the user being dangerously cold.
 9. The hearable of claim 1, wherein the sensor is selected from the list of sensors consisting of: a microphone, a barometric pressure sensor, and a motion sensor.
 10. A method of hearable use for a hearable comprising a wearable structure at least a portion of which is configured to be disposed within an ear of a user of the hearable when the hearable is in use by the user, the method comprising: acquiring, by a processor, temperature data in a form of a plurality of temperature measurements captured from a temperature compensating circuit while the portion of the wearable structure is disposed within the ear of the user, wherein the temperature compensating circuit is coupled with a sensor of the hearable and configured to measure temperature in an environment of the sensor for use in temperature compensation of operation of the sensor, and wherein the temperature compensating circuit is disposed on the portion of the wearable structure which is configured to be disposed within the ear of the user; building, by the processor, a baseline model of normal temperature for the user from the plurality of temperature measurements; comparing, by the processor, a temperature measurement acquired from the temperature compensating circuit to the baseline model of normal temperature for the user; and responsive to the comparison showing a deviation beyond a preset threshold from the baseline model, generating, by the processor, a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user.
 11. The method as recited in claim 10, further comprising: acquiring, by the processor, a second temperature measurement from a second temperature compensating circuit, wherein the second temperature compensating circuit is coupled with a second sensor of the hearable and configured to measure the second temperature in an environment of the second sensor for use in temperature compensation of operation of the second sensor, wherein the second sensor is disposed on a second portion of the wearable structure, wherein the second temperature measurement is acquired contemporaneously with acquisition of the temperature measurement, wherein the second temperature compensating circuit is disposed on a second portion of the wearable structure which is configured to be disposed outside the ear of the user when the hearable is in use by the user; computing, by the processor, a temperature of the user based on the temperature measurement and the second temperature measurement; comparing, by the processor, the computed temperature of the user to the baseline model of normal temperature for the user; and responsive to the comparison of the computed temperature showing a second deviation beyond the preset threshold from the baseline model, generating, by the processor, the health indicator for the user based on the second deviation.
 12. The method as recited in claim 10, wherein the method further comprises: reporting, by the processor, the health indicator to a device selected by the user.
 13. The method as recited in claim 10, wherein the generating, by the processor, a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user comprises: generating, by the processor, an audible report of the health indicator which is output by a speaker of the hearable.
 14. The method as recited in claim 10, wherein the generating, by the processor, a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user comprises: generating, by the processor, an indicator of the user having a fever.
 15. The method as recited in claim 10, wherein the generating, by the processor, a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user comprises: generating, by the processor, an indicator of the user experiencing ovulation.
 16. The method as recited in claim 10, wherein the generating, by the processor, a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user comprises: generating, by the processor, an indicator of the user experiencing heat stress.
 17. The method as recited in claim 10, wherein the generating, by the processor, a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user comprises: generating, by the processor, an indicator of the user being dangerously cold.
 18. A sensor processing unit comprising: a memory; a sensor; a temperature compensating circuit coupled with the sensor and configured to measure a temperature in an environment of the sensor for use in temperature compensation of operation of the sensor; and a sensor processor coupled with the memory, the temperature compensating circuit, and the sensor, wherein the sensor processor is configured to: acquire temperature data in a form of a plurality of temperature measurements captured from the temperature compensating circuit while a portion of a hearable in which the temperature compensating circuit is located is disposed within an ear of a user; build a baseline model of normal temperature for the user from the plurality of temperature measurements; compare a temperature measurement acquired from the temperature compensating circuit to the baseline model of normal temperature for the user; and responsive to the comparison showing a deviation beyond a preset threshold from the baseline model, generate a health indicator for the user based on the deviation, wherein the health indicator is used to monitor an aspect of health of the user.
 19. The sensor processing unit of claim 18, further comprising: a second sensor; a second temperature compensating circuit coupled with the second sensor and configured to measure a second temperature in an environment of the second sensor for use in temperature compensation of operation of the second sensor; wherein the second sensor and the second temperature compensating circuit are coupled with the sensor processor, and the sensor processor is further configured to: acquire a second temperature measurement from the second temperature compensating circuit while a second portion of a hearable in which the second temperature compensating circuit is located is disposed outside the ear of the user, and wherein the second temperature measurement is acquired contemporaneously with acquisition of the temperature measurement; compute a temperature of the user based on the temperature measurement and the second temperature measurement; compare the computed temperature of the user to the baseline model of normal temperature for the user; and responsive to the comparison of the computed temperature showing a second deviation beyond the preset threshold from the baseline model, generate the health indicator for the user based on the second deviation.
 20. The sensor processing unit of claim 18, wherein the sensor processor is further configured to: report the health indicator to a device external to the sensor processing unit. 